ub.xmlui.mirage2.page-structure.muninLogoub.xmlui.mirage2.page-structure.openResearchArchiveLogo
    • EnglishEnglish
    • norsknorsk
  • Velg spraaknorsk 
    • EnglishEnglish
    • norsknorsk
  • Administrasjon/UB
Vis innførsel 
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for teknologi og sikkerhet
  • Artikler, rapporter og annet (teknologi og sikkerhet)
  • Vis innførsel
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for teknologi og sikkerhet
  • Artikler, rapporter og annet (teknologi og sikkerhet)
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Comparative study of data-driven short-term wind power forecasting approaches for the Norwegian Arctic region

Permanent lenke
https://hdl.handle.net/10037/24533
DOI
https://doi.org/10.1063/5.0038429
Thumbnail
Åpne
article.pdf (941.3Kb)
Akseptert manusversjon (PDF)
Dato
2021-04-26
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Forfatter
Chen, Hao; Birkelund, Yngve; Anfinsen, Stian Normann; Yuan, Fuqing
Sammendrag
This paper conducts a systemic comparative study on univariate and multivariate wind power forecasting for five wind farms inside the Arctic area. The development of wind power in the Arctic can help reduce greenhouse gas emissions in this environmentally fragile region. In practice, wind power forecasting is essential to maintain the grid balance and optimize electricity generation. This study first applies various learning methods for wind power forecasting. It comprehensively compares the performance of models categorized by whether considering weather factors in the Arctic. Nine different representative types of machine-learning algorithms make several univariate time series forecasting, and their performance is evaluated. It is demonstrated that machine-learning approaches have an insignificant advantage over the persistence method in the univariate situation. With numerical weather prediction wind data and wind power data as inputs, the multivariate forecasting models are established and made one to six h in advance predictions. The multivariate models, especially with the advanced learning algorithms, show their edge over the univariate model based on the same algorithm. Although weather data are mesoscale, they can contribute to improving the wind power forecasting accuracy. Moreover, these results are generally valid for the five wind farms, proving the models' effectiveness and universality in this regional wind power utilization. Additionally, there is no clear evidence that predictive model performance is related to wind farms' topographic complexity.
Er en del av
Chen, H. (2022). Data-driven Arctic wind energy analysis by statistical and machine learning approaches. (Doctoral thesis). https://hdl.handle.net/10037/26938
Forlag
American Institute of Physics
Sitering
Chen, Birkelund, Anfinsen, Yuan. Comparative study of data-driven short-term wind power forecasting approaches for the Norwegian Arctic region. Journal of Renewable and Sustainable Energy. 2021;13(2)
Metadata
Vis full innførsel
Samlinger
  • Artikler, rapporter og annet (teknologi og sikkerhet) [361]
Copyright 2021 The Author(s)

Bla

Bla i hele MuninEnheter og samlingerForfatterlisteTittelDatoBla i denne samlingenForfatterlisteTittelDato
Logg inn

Statistikk

Antall visninger
UiT

Munin bygger på DSpace

UiT Norges Arktiske Universitet
Universitetsbiblioteket
uit.no/ub - munin@ub.uit.no

Tilgjengelighetserklæring